The impact of land use and land cover on groundwater fluctuations using remote sensing and geographical information system: Representative case study in Afghanistan

Ziaul Haq Doost () and Zaher Mundher Yaseen ()
Additional contact information
Ziaul Haq Doost: King Fahd University of Petroleum and Minerals
Zaher Mundher Yaseen: King Fahd University of Petroleum and Minerals

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 4, No 50, 9515-9538

Abstract: Abstract As urbanization continues to accelerate, managing groundwater resources sustainably has become a momentous challenge for many cities, including Kabul. This study seeks to investigate the profound impact of land use and land cover (LULC) on groundwater fluctuations in the capital of Afghanistan. The present research relied on remote-sensed images and groundwater table data analyzed using an advanced geographic information system (GIS) environment. To develop LULC maps, two distinct models of Landsat image classification (supervised and unsupervised) were employed. The research practiced three time periods (2000, 2013, and 2020) and four crucial themes (bare land, built-up area, vegetation, and water bodies) to develop LULC maps. Moreover, the current study used two time intervals (2016 and 2020) for groundwater level maps within the region. The results indicate the superiority of the supervised Landsat image classification model by putting to use the support vector machine (SVM) technique. This approach yielded noticeably higher accuracies, with outcomes of 94.23%, 90.09%, and 88.18% for 2000, 2013, and 2020, respectively. The unsupervised Landsat image classification model, conversely, revealed results of 89%, 82.5%, and 84.26% for the same periods. To validate the accuracy of LULC maps and interpolation for groundwater table maps, taking advantage of the confusion matrix and cross-validation techniques, respectively. Findings clearly support the significant impact of LULC on groundwater fluctuations in Kabul city. The preeminent accuracy of the supervised Landsat image classification model provides powerful evidence for its effectiveness in precisely assessing the impacts of LULC on groundwater fluctuations.

Keywords: Image classification; Machine learning models; Groundwater fluctuations; Kabul city; Afghanistan (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-023-04253-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:4:d:10.1007_s10668-023-04253-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-023-04253-2

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().